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Group Analysis with AFNI - Hands On

Group Analysis with AFNI - Hands On. The following sample group analysis comes from “How-to #5 -- Group Analysis: AFNI 3dANOVA3”, described in full detail on the AFNI website: http://afni.nimh.gov/pub/dist/HOWTO/howto/ht05_group/html Brief description of experiment : Design:

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Group Analysis with AFNI - Hands On

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  1. Group Analysis with AFNI - Hands On • The following sample group analysis comes from “How-to #5 -- Group Analysis: AFNI 3dANOVA3”, described in full detail on the AFNI website: http://afni.nimh.gov/pub/dist/HOWTO/howto/ht05_group/html • Brief description of experiment: • Design: • Rapid event-related • “Stimulus Condition” has 4 levels: • TM = Tool Movies • HM = Human Movies • TP = Tool Point Light Displays • HP = Human Point Light Displays Tool Movie Human Movie Tool Point Light Human Point Light

  2. Data Collected: • 1 Anatomical (SPGR) dataset for each subject • 124 sagittal slices • 10 Time Series (EPI) datasets for each subject • 23 axial slices x 138 volumes = 3174 volumes/timepoints per run • note: each run consists of random presentations of rest and all 4 stimulus condition levels • TR = 2 sec; voxel dimensions = 3.75 x 3.75 x 5 mm • Sample size, n=7 (subjects ED, EE, EF, FH, FK, FL, FN) • Analysis Steps: • Part I: Process data for each subject first • Pre-process subjects’ data  many steps involved here… • Run deconvolution analysis on each subject’s dataset --- 3dDeconvolve • Part II: Run group analysis • 3-way Analysis of Variance (ANOVA) --- 3dANOVA3 • i.e., Object Type (2) x Animation Type (2) x Subjects (7) = 3-way ANOVA

  3. PART I  Process Data for each Subject First: • Hands-on example: Subject ED • We will begin with ED’s anat dataset and 10 time-series (3D+time) datasets: EDspgr+orig, EDspgr+tlrc, ED_r01+orig, ED_r02+orig … ED_r10+orig • Below is ED’s ED_r01+orig (3D+time) dataset. Notice the first two time points of the time series have relatively high intensities*. We will need to remove them later: Timepoints 0 and 1 have high intensity values • Images obtained during the first 4-6 seconds of scanning will have much larger intensities than images in the rest of the timeseries, when magnetization (and therefore intensity) has decreased to its steady state value

  4. STEP 1: Check for possible “outliers” in each of the 10 time series datasets. The AFNI program to use is 3dToutcount (also run by default in to3d) • An outlier is usually seen as an isolated spike in the data, which may be due to a number of factors, such as subject head motion or scanner irregularities. • In any case, the outlier is not a true signal that results from presentation of a stimulus event, but rather, an artifact from something else -- it is noise. foreach run (01 02 03 04 05 06 07 08 09 10) 3dToutcount -automask ED_r{$run}+orig \ > toutcount_r{$run}.1D end • How does this program work? For each time series, the trend and Mean AbsoluteDeviation are calculated. Points far away from the trend are considered outliers. “Far away” is mathematically defined. • See 3dToutcount -help for specifics. • -automask: Does the outlier check only on voxels within the brain and ignores background voxels (which are detected by the program because of their smaller intensity values). • > : This is the “redirect” symbol in UNIX. Instead of displaying the results onto the screen, they are saved into a text file. In this example, the text files are called toutcount_r{$run}.1D.

  5. Note: “1D” is used to identify a text file. In this case, each file consists a column of 138 numbers (b/c of 138 time points). • Subject ED’s outlier files: toutcount_r01.1D toutcount_r02.1D … toutcount_r10.1D • Use AFNI 1dplot to display any one of ED’s outlier files. For example: 1dplot toutcount_r04.1D Num. of ‘outlier’ voxels Outliers? If head motion, this should be cleared up with 3dvolreg. If due to something weird with the scanner, 3dDespike might work (but use sparingly). High intensity values in the beginning are usually due to scanner attempting to reach steady state. time

  6. STEP 2: Shift voxel time series so that separate slices are aligned to the same temporal origin using3dTshift • The temporal alignment is done so it seems that all slices were acquired at the same time, i.e., the beginning of each TR. • The output dataset time series will be interpolated from the input to a new temporal grid. There are several interpolation methods to choose from, including ‘Fourier’, ‘linear’, ‘cubic’, ‘quintic’, and ‘heptic’. foreach run (01 02 03 04 05 06 07 08 09 10) 3dTshift -tzero 0 -heptic \ -prefix ED_r{$run}_ts \ ED_r{$run}+orig end • -tzero: Tells the program which slice’s time offset to align to. In this example, the slices are all aligned to the time offset of the first (0) slice. • -heptic: Use the 7th order Lagrange polynomial interpolation. Why 7th order? Bob Cox likes this (and that’s good enough for me).

  7. Subject ED’s newly created time shifted datasets: ED_r01_ts+orig.HEAD ED_r01_ts+orig.BRIK … … ED_r10_ts+orig.HEAD ED_r10_ts+orig.BRIK • Below is run 01 of ED’s time shifted dataset, ED_r01_ts+orig: 6 4 Slice acquisition now in synchrony with beginning of TR 2

  8. STEP 3: Volume Register the voxel time series for each 3D+time dataset using AFNI program3dvolreg • We will also remove the first 2 time points at this step foreach run (01 02 03 04 05 06 07 08 09 10) 3dvolreg -verbose -base ED_r01_ts+orig’[2]’ \ -prefix ED_r{$run}_vr \ -1Dfile dfile.r{$run}.1D \ ED_r{$run}_ts+orig’[2..137]’ end • -verbose: Prints out progress report onto screen • -base: Timepoint 2 is our base/target volume to which the remaining timepoints (3-137) will be aligned. We are ignoring timepoints 0 and 1 • -prefix gives our output files a new name, e.g., ED_r01_vr+orig • -1Dfile: Save motion parameters for each run (roll, pitch, yaw, dS, dL, dP) into a file containing 6 ASCII formatted columns. • ED_r{$run}_ts+orig’[2..137]’ refers to our input datasets (runs 01-10) that will be volume registered. Notice that we are removing timepoints 0 and 1

  9. Subject ED’s newly created volume registered datasets: ED_r01_vr+orig.HEAD ED_r01_vr+orig.BRIK … … ED_r10_vr+orig.HEAD ED_r10_vr+orig.BRIK • Below is run 01 of ED’s volume registered datasets, ED_r01_vr+orig:

  10. STEP 4: Smooth 3D+time datasets with AFNI 3dmerge • The result of spatial blurring (filtering) is somewhat cleaner, more contiguous activation blobs • Spatial blurring will be done on ED’s time shifted, volume registered datasets: foreach run (01 02 03 04 05 06 07 08 09 10) 3dmerge -1blur_fwhm 4 \ -doall \ -prefix ED_r{$run}_vr_bl \ ED_r{$run}_vr+orig end • -1blur_fwhm 4 sets the Gaussian filter to have a full width half max of 4mm (You decide the type of filter and the width of the selected filter) • -doall applies the editing option (in this case the Gaussian filter) to all sub-bricks uniformly in each dataset

  11. Result from 3dmerge: ED_r01_vr+orig ED_r01_vr_bl+orig Before blurring After blurring

  12. STEP 5: Scaling the Data - i.e., Calculating Percent Change • This particular step is a bit more involved, because it is comprised of three parts. Each part will be described in detail: • Create a mask so that all background values (outside of the volume) are set to zero with 3dAutomask • Do a voxel-by-voxel calculation of the mean intensity value with 3dTstat • Do a voxel-by-voxel calculation of the percent signal change with 3dcalc • Why should we scale our data? • Scaling becomes an important issue when comparing data across subjects, because baseline/rest states will vary from subject to subject • The amount of activation in response to a stimulus event will also vary from subject to subject • As a result, the baseline Impulse Response Function (IRF) and the stimulus IRF will vary from subject to subject -- we must account for this variability • By converting to percent change, we can compare the activation calibrated with the relative change of signal, instead of the arbitrary baseline of FMRI signal

  13. For example: Subject 1 - Signal in hippocampus goes from 1000 (baseline) to 1050 (stimulus condition) Difference = 50 IRF units Subject 2 - Signal in hippocampus goes from 500 (baseline) to 525 (stimulus condition) Difference = 25 IRF units • Conclusion: • Subject 1 shows twice as much activation in response to the stimulus condition than does Subject 2 --- WRONG!! • If ANOVA were run on these difference scores, the change in baseline from subject to subject would add variance to the analysis • We must control for these differences in baseline across subjects by somehow normalizing the baseline so that a reliable comparison between subjects can be made

  14. Solution: • Compute Percent Signal Change • i.e., by what percent does the Impulse Response Function increase with presentation of the stimulus condition, relative to baseline? • Percent Change Calculation: • If A = Stimulus IRF • If B = Baseline IRF Percent Signal Change = (A/B) * 100%

  15. Subject 1 -- Stimulus (A) = 1050, Baseline (B) = 1000 (1050/1000) * 100% = 105% or 5% increase in IRF • Subject 2 -- Stimulus (A) = 525, Baseline (B) = 500 (525/500) * 100% = 105% or 5% increase in IRF • Conclusion: • Both subjects show a 5% increase in signal change from baseline to stimulus condition • Therefore, no significant difference in signal change between these two subjects

  16. STEP 5A: Ignore any background values in a dataset by creating a mask with 3dAutomask • Values in the background have very low baseline values, which can lead to artificially large percent signal change values. Let’s remove them altogether by creating a mask of our dataset, where values inside the brain are assigned a value of “1” and values outside of the brain (e.g., noise) are assigned a value of “0” • This mask will be used later when the percent signal change in each voxel is calculated. A percent change will be computed only for voxels inside the mask • A mask will be created for each of Subject ED’s time shifted/volume registered/blurred 3D+time datasets: foreach run (01 02 03 04 05 06 07 08 09 10) 3dAutomask -dilate 1 -prefix mask_r{$run} \ ED_r{$run}_vr_bl+orig end • Output of 3dAutomask: A mask dataset for each 3D+time dataset: mask_r01+orig, mask_r02+orig … mask_r10+orig

  17. Now let’s take those 10 masks (we don’t need 10 separate masks) and combine them to make one master or “full mask”, which will be used to calculate the percent signal change only for values inside the mask (i.e., inside the brain). • 3dcalc -- one of the most versatile AFNI programs -- is used to combine the 10 masks into one: 3dcalc-a mask_r01+orig -b mask_r02+orig -c mask_r03+orig \ -d mask_r04+orig -e mask_r05+orig -f mask_r06+orig \ -g mask_r07+orig -h mask_r08+orig -i mask_r09+orig \ -j mask_r10+orig \ -expr ‘ispositive(a+b+c+d+e+f+g+h+i+j)’ \ -prefix full_mask Output: full_mask+orig: • -expr ‘ispositive’: Used to determine whether voxels along the edges make it to the full mask or not. If an edge voxel has a “1” value in any of the individual masks, the ‘ispositive’ keeps that voxel as part of the full mask.

  18. STEP 5B: Create a voxel-by-voxel mean for each timeseries dataset with 3dTstat • For each voxel, add the intensity values of the 136 time points and divide by 136 • The resulting mean will be inserted into the “B” slot of our percent signal change equation (A/B*100%) foreach run (01 02 03 04 05 06 07 08 09 10) 3dTstat -prefix mean_r{$run} \ ED_r{$run}_vr_bl+orig end • Unless otherwise specified, the default statistic for 3dTstat is to compute a voxel-by-voxel mean • Other statistics run by 3dTstat include a voxel-by-voxel standard deviation, slope, median, etc…

  19. The end result will be a dataset consisting of a single mean value in each voxel. Below is a graph of a 3x3 voxel matrix from subject ED’s dataset mean_r01+orig: ED_r01_vr_bl+orig Timept 0: 1530 + TP 1: 1515 + TP 2: 1498 + TP … + TP 135: 1522 Divide sum by 136 mean_r01+orig Mean = 1523.346

  20. STEP 5C: Calculate a voxel-by-voxel percentile signal value with 3dcalc • Take the 136 intensity values within each voxel, divide each one by the mean intensity value for that voxel (that we calculated in Step 3B), and multiply by 100 to get a percent signal change at each timepoint • This is where the A/B*100 equation comes into play foreach run (01 02 03 04 05 06 07 08 09 10) 3dcalc -a ED_r{$run}_vr_bl+orig \ -b mean_r{$run}+orig \ -c full_mask+orig \ -expr “(a/b * 100) * c” \ -prefix scaled_r{$run} end • Output of 3dcalc: 10 scaled datasets for Subject ED, where the signal intensity value at each timepoint has now been replaced with a percent signal value scaled_r01+orig, scaled_r02+orig …scaled_r10+orig

  21. scaled_r01+orig • E.g., Timepoint #18 above shows a percentile signal value of 101.7501 • i.e., relative to the baseline (of 100), the stimulus presentation (and noise too) resulted in a percent signal change of 1.7501% at that specific timepoint Timepoint #18 Displays the timepoint highlighted in center voxel and its percentile signal value Shows index coordinates for highlighted voxel

  22. STEP 6: Concatenate ED’s 10 scaled datasets into onebig dataset with 3dTcat 3dTcat -prefix ED_all_runs \ scaled_r??+orig.HEAD • The ?? Takes the place of having to type out each individual run, such as scaled_01+orig, scaled_r02+orig, etc. This is a helpful UNIX shortcut. You could also use the wildcard * • The output from 3dTcat is one big dataset -- ED_all_runs+orig -- which consists of 1360 volumes (i.e., 10 runs x 136 timepoints). Every voxel in this large dataset contains percent signal change values • This output file will be inserted into the3dDeconvolve program • Do you recall those motion parameter files we created when running 3dvolreg? (No? See page 8 of this handout). We need to concatenate those files too because they will be inserted into the 3dDeconvolve command as Regressors of No Interest (RONI’s). • The UNIX program cat will concatenate these ASCII files: cat dfile.r??.1D > dfile.all.1D

  23. STEP 7: Perform a deconvolution analysis on Subject ED’s data with 3dDeconvolve • What is the difference between regular linear regression and deconvolution analysis? • With linear regression, the hemodynamic response is already assumed (we can get a fixed hemodynamic model by running the AFNI waver program) • With deconvolution analysis, the hemodynamic response is not assumed. Instead, it is computed by3dDeconvolve from the data • Once the HRF is modeled by 3dDeconvolve, the program then runs a linear regression on the data • To compute the hemodynamic response function with 3dDeconvolve, we include the “minlag” and “maxlag” options on the command line • The user (you) must determine the lag time of an input stimulus • 1 lag = 1 TR = 2 seconds • In this example, the lag time of the input stimulus has been determined to be about 15 lags (decided by the wise and all-knowing experimenter) • As such, we will add a “minlag” of 0 and a “maxlag” of 14 in our 3dDeconvolve command

  24. Our baseline is quadratic (default is “linear”) Concatenated 10 runs for subject ED Our stim files for each stim condition • 3dDeconvolve command - Part 1 3dDeconvolve -polort 2 -input ED_all_runs+orig -num_stimts 10 \ -concat ../misc_files/runs.1D \ -stim_file 1 ../misc_files/all_stims.1D’[0]’ \ -stim_label 1ToolMovie \ -stim_minlag 1 0 -stim_maxlag 1 14 -stim_nptr 1 2 \ -stim_file 2 ../misc_files/all_stims.1D’[1]’ \ -stim_label 2 HumanMovie \ -stim_minlag 2 0 -stim_maxlag 2 14 -stim_nptr 2 2 \ -stim_file 3 ../misc_files/all_stims.1D’[2]’ \ -stim_label 3 ToolPoint \ -stim_minlag 3 0 -stim_maxlag 3 14 -stim_nptr 3 2 \ -stim_file 4 ../misc_files/all_stims.1D’[3]’ \ -stim_label 4 HumanPoint \ -stim_minlag 4 0 -stim_maxlag 4 14-stim_nptr 4 2 \ # of stimulus function pts per TR. Default = 1. Here it’s set to 2 Experimenter estimates the HRF will last ~15 sec Continued on next page…

  25. RONI’s are part of the baseline model RONI’s • 3dDeconvolve command - Part 2 -stim_file 5 dfile.all.1D’[0]’-stim_base 5 \ -stim_file 6 dfile.all.1D’[1]’ -stim_base 6 \ -stim_file 7 dfile.all.1D’[2]’ -stim_base 7 \ -stim_file 8 dfile.all.1D’[3]’ -stim_base 8 \ -stim_file 9 dfile.all.1D’[4]’ -stim_base 9 \ -stim_file 10 dfile.all.1D’[5]’ -stim_base 10 \ -gltsym ../misc_files/contrast1.1D -glt_label 1 FullF \ -gltsym ../misc_files/contrast2.1D -glt_label 2 HvsT \ -gltsym ../misc_files/contrast3.1D -glt_label 3 MvsP \ -gltsym ../misc_files/contrast4.1D -glt_label 4 HMvsHP \ -gltsym ../misc_files/contrast5.1D -glt_label 5 TMvsTP \ -gltsym ../misc_files/contrast6.1D -glt_label 6 HPvsTP \ -gltsym ../misc_files/contrast7.1D -glt_label 7 HMvsTM \ General Linear Tests, “Symbolic” usage. E.g., +HumanMovie -ToolMovie rather than -glt option, e.g., 30@0 1 -1 0 0 Continued on next page…

  26. iresp files show the voxel-by-voxel impulse response function for each stimulus condition. Recall that the IRF was modeled using ‘min’ and ‘max’ lag options (more explanation on p.27). • 3dDeconvolve command - Part 3 -iresp 1 TMirf -iresp 2 HMirf -iresp 3 TPirf -iresp 4 HPirf -full_first -fout -tout -nobout -xpeg Xmat -bucket ED_func Show Full-F first in bucket dataset, compute F-tests, compute t-tests, don’t show output of baseline coefficients in bucket dataset Writes a JPEG file graphing the X matrix Done with 3dDeconvolve command

  27. -iresp 1 TMirf • -iresp 2 HMirf • -iresp 3 TPirf • -iresp 4 HPirf • These output files are important because they contain the estimated Impulse Response Function for each stimulus type • The percent signal change is shown at each time lag • Below is the estimated IRF for Subject ED’s “Human Movies” (HM) condition: Switch UnderLay: HMirf+orig Switch OverLay: ED_func+orig

  28. Focusing on a single voxel (from ED’s HMirf+orig dataset), we can see that the IRF is made up of 15 time lags (0-14). Recall that this lag duration was determined in the 3dDeconvolve command • Each time lag consists of a percent signal change value:

  29. To run an ANOVA, only one data point can exist in each voxel • As such, the percent signal change values in the 15 lags must be averaged • In the voxel displayed below, the mean percent signal change =1.957% Mean % sig. chg,. (lags 0-14) = 1.957% +

  30. STEP 8: Compute a voxel-by-voxel mean percent signal change with AFNI 3dTstat • The following 3dTstat command will compute a voxel-by-voxel mean for each IRF dataset, of which we have four: TMirf, HMirf, TPirf, HPirf foreach cond (TM HM TP HP) 3dTstat -prefix ED_{$cond}_irf_mean \ {$cond}irf+orig end

  31. The output from 3dTstat will be four irf_mean datasets, one for each stimulus type. Below are subject ED’s averaged IRF datasets: ED_TM_irf_mean+orig ED_HM_irf_mean+orig ED_TP_irf_mean+orig ED_HP_irf_mean+orig • Each voxel will now contain a single number (i.e., the mean percent signal change). For example: ED_HM_irf_mean+orig

  32. STEP 9: Resample the mean IRF datasets for each subject to the same grid as their Talairached anatomical datasets with adwarp • For statistical comparisons made across subjects, all datasets -- including functional overlays -- should be standardized (e.g., Talairach format) to control for variability in brain shape and size foreach cond (TM HM TP HP) adwarp -apar EDspgr+tlrc -dxyz 3 \ -dpar ED_{$cond}_irf_mean+orig \ end • The output of adwarp will be four Talairach transformed IRF datasets. ED_TM_irf_mean+tlrc ED_HM_irf_mean+tlrc ED_TP_irf_mean+tlrc ED_HP_irf_mean+tlrc • We are now done with Part 1-- Process Individual Subjects’ Data -- for Subject ED • Go back and follow the same steps for remaining 6 subjects • We can now move on to Part 2 -- RUN GROUP ANALYSIS (ANOVA)

  33. PART 2  Run Group Analysis (ANOVA3): • In our sample experiment, we have 3 factors (or Independent Variables) for our analysis of variance: “Stimulus Condition” and “Subjects” • IV 1: OBJECT TYPE  2 levels • Tools (T) • Humans (H) • IV 2: ANIMATION TYPE  2 levels • Movies (M) • Point-light displays (P) • IV 3: SUBJECTS  7 levels (note: this is a small sample size!) • Subjects ED, EE, EF, FH, FK, FL, FN • The mean IRF datasets from each subject will be needed for the ANOVA. Example: ED_TM_irf_mean+tlrcEE_TM_irf_mean+tlrcEF_TM_irf_mean+tlrc ED_HM_irf_mean+tlrcEE_HM_irf_mean+tlrcEF_HM_irf_mean+tlrc ED_TP_irf_mean+tlrcEE_TP_irf_mean+tlrcEF_TP_irf_mean+tlrc ED_HP_irf_mean+tlrcEE_HP_irf_mean+tlrcEF_HP_irf_mean+tlrc

  34. IV’s A & B are fixed, C is random. See 3dANOVA3 -help • 3dANOVA3 Command - Part 1 3dANOVA3 -type 4 \ -alevels 2 \ -blevels 2 \ -clevels 7 \ -dset 1 1 1 ED_TM_irf_mean+tlrc \ -dset 2 1 1 ED_HM_irf_mean+tlrc \ -dset 1 2 1 ED_TP_irf_mean+tlrc \ -dset 2 2 1 ED_HP_irf_mean+tlrc \ -dset 1 1 2 EE_TM_irf_mean+tlrc \ -dset 2 1 2 EE_HM_irf_mean+tlrc \ -dset 1 2 2 EE_TP_irf_mean+tlrc \ -dset 2 2 2 EE_HP_irf_mean+tlrc \ -dset 1 1 3 EF_TM_irf_mean+tlrc \ -dset 2 1 3 EF_HM_irf_mean+tlrc \ -dset 1 2 3 EF_TP_irf_mean+tlrc \ -dset 2 2 3 EF_HP_irf_mean+tlrc \ IV A: Object IV B: Animation IV C: Subjects irf datasets, created for each subj with 3dDeconvolve (See p.26) Continued on next page…

  35. 3dANOVA3 Command - Part 2 -dset 1 1 4 FH_TM_irf_mean+tlrc \ -dset 2 1 4 FH_HM_irf_mean+tlrc \ -dset 1 2 4 FH_TP_irf_mean+tlrc \ -dset 2 2 4 FH_HP_irf_mean+tlrc \ -dset 1 1 5 FK_TM_irf_mean+tlrc \ -dset 2 1 5 FK_HM_irf_mean+tlrc \ -dset 1 2 5 FK_TP_irf_mean+tlrc \ -dset 2 2 5 FK_HP_irf_mean+tlrc \ -dset 1 1 6 FL_TM_irf_mean+tlrc \ -dset 2 1 6 FL_HM_irf_mean+tlrc \ -dset 1 2 6 FL_TP_irf_mean+tlrc \ -dset 2 2 6 FL_HP_irf_mean+tlrc \ -dset 1 1 7 FN_TM_irf_mean+tlrc \ -dset 2 1 7 FN_HM_irf_mean+tlrc \ -dset 1 2 7 FN_TP_irf_mean+tlrc \ -dset 2 2 7 FN_HP_irf_mean+tlrc \ more irf datasets Continued on next page…

  36. Produces main effect for factor ‘a’ (Object type), i.e., which voxels show increases in % signal change that is significantly different from zero? • 3dANOVA3 Command - Part 3 -fa ObjEffect \ -fb AnimEffect \ -adiff 1 2 TvsH \ -bdiff 1 2 MvsP \ -acontr 1 -1 sameas.TvsH \ -bcontr 1 -1 sameas.MvsP \ -aBcontr 1 -1: 1 TMvsHM \ -aBcontr -1 1: 2 HPvsTP \ -Abcontr 1: 1 -1 TMvsTP \ -Abcontr 2: 1 -1 HMvsHP \ -bucket AvgAnova Main effect for factor ‘b’, (Animation type) These are contrasts (t-tests). Explained on pp 38-39 All F-tests, t-tests, etc will go into this dataset bucket End of ANOVA command

  37. -adiff: Performs contrasts between levels of factor ‘a’ (or -bdiff for factor ‘b’, -cdiff for factor ‘c’, etc), with no collapsing across levels of factor ‘a’. E.g.1, Factor “Object Type” --> 2 levels: (1)Tools, (2)Humans: -adiff 1 2 TvsH E.g., 2, Factor “Faces” --> 3 levels: (1)Happy, (2)Sad, (3)Neutral -adiff 1 2 HvsS -adiff 2 3 SvsN -adiff 1 3 HvsN • -acontr: Estimates contrasts among levels of factor ‘a’ (or -bcontr for factor ‘b’, -ccontr for factor ‘c’, etc). Allows for collapsing across levels of factor ‘a’ • In our example, since we only have 2 levels for both factors ‘a’ and ‘b’, the -diff and -contr options can be used interchangeably. Their different usages can only be demonstrated with a factor that has 3 or more levels: • E.g.: factor ‘a’ = FACES --> 3 levels :(1) Happy, (2) Sad, (3) Neutral -acontr -1 .5 .5 HvsSN -acontr .5 .5 -1 HSvsN -acontr .5 -1 .5 HNvsS Simple paired t-tests, no collapsing across levels, like Happy vs. Sad/Neutral Happy vs. Sad/Neutral Happy/Sad vs. Neutral Happy/Neutral vs. Sad

  38. -aBcontr: 2nd order contrast. Performs comparison between 2 levels of factor ‘a’ at a Fixed level of factor ‘B’ • E.g. factor ‘a’ --> Tools(1) vs. Humans(-1), factor ‘B’ --> Movies(1) vs. Points(2) • We want to compare ‘Tools Movies’ vs. ‘Human Movies’. Ignore ‘Points’ -aBcontr 1 -1 : 1 TMvsHM • We want to compare “Tool Points’ vs. ‘Human Points’. Ignore ‘Movies’ -aBcontr 1 -1 : 2 TPvsHP • -Abcontr: 2nd order contrast. Performs comparison between 2 levels of factor ‘b’ at a Fixed level of factor ‘A’ • E.g., E.g. factor ‘b’ --> Movies(1) vs. Points(-1), factor ‘A’ --> Tools(1) vs. Humans(2) • We want to compare ‘Tools Movies’ vs. ‘Tool Points’. Ignore ‘Humans -Abcontr 1 : 1 -1 TMvsTP • We want to compare “Human Movies vs. ‘Human Points’. Ignore ‘Tools’ -Abcontr 2 : 1 -1 HMvsHP

  39. In class -- Let’s run the ANOVA together: • cd AFNI_data2 • This directory contains a script called s3.anova.ht05 that will run 3dANOVA3 • This script can be viewed with a text editor, like emacs • ./s3.anova.ht05 • execute the ANOVA script from the command line • cd group_data ; ls • result from ANOVA script is a bucket dataset AvgANOVA+tlrc, stored in the group_data/ directory • afni & • launch AFNI to view the results • The output from 3dANOVA3 is bucket dataset AvgANOVA+tlrc, which contains 20 sub-bricks of data: • i.e., main effect F-tests for factors A and B, 1st order contrasts, and 2nd order contrasts

  40. -fa: Produces a main effect for factor ‘a’ • In this example, -fa determines which voxels show a percent signal change that is significantly different from zero when any level of factor “Object Type” is presented • -fa ObjEffect: ULay: sample_anat+tlrc OLay: AvgANOVA+tlrc Activated areas respond to OBJECTS in general (i.e., humans and/or tools)

  41. Brain areas corresponding to “Tools” (reds) vs. “Humans” (blues) • -diff 1 2 TvsH (or -acontr 1 -1 TvsH) ULay: sample_anat+tlrc OLay: AvgANOVA+tlrc Red blobs show statistically significant percent signal changes in response to “Tools.” Blue blobs show significant percent signal changes in response to “Humans” displays

  42. Brain areas corresponding to “Human Movies” (reds) vs. “Humans Points” (blues) • -Abcontr 2: 1 -1 HMvsHP ULay: sample_anat+tlrc OLay: AvgANOVA+tlrc Red blobs show statistically significant percent signal changes in response to “Human Movies.” Blue blobs show significant percent signal changes in response to “Human Points” displays

  43. Many thanks to Mike Beauchamp for donating the data used in this lecture and in the how-to#5 • For a full review of the experiment described in this lecture, see Beauchamp, M.S., Lee, K.E., Haxby, J.V., & Martin, A. (2003). FMRI responses to video and point-light displays of moving humans and manipulable objects. Journal of Cognitive Neuroscience, 15:7, 991-1001. • For more information on AFNI ANOVA programs, visit the web page of Gang Chen, our wise and infinitely patient statistician: http//afni.nimh.gov/sscc/gangc

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